An Automatic Digital Audio Authentication/Forensics System

With the continuous rise in ingenious forgery, a wide range of digital audio authentication applications are emerging as a preventive and detective control in real-world circumstances, such as forged evidence, breach of copyright protection, and unauthorized data access. To investigate and verify, this paper presents a novel automatic authentication system that differentiates between the forged and original audio. The design philosophy of the proposed system is primarily based on three psychoacoustic principles of hearing, which are implemented to simulate the human sound perception system. Moreover, the proposed system is able to classify between the audio of different environments recorded with the same microphone. To authenticate the audio and environment classification, the computed features based on the psychoacoustic principles of hearing are dangled to the Gaussian mixture model to make automatic decisions. It is worth mentioning that the proposed system authenticates an unknown speaker irrespective of the audio content i.e., independent of narrator and text. To evaluate the performance of the proposed system, audios in multi-environments are forged in such a way that a human cannot recognize them. Subjective evaluation by three human evaluators is performed to verify the quality of the generated forged audio. The proposed system provides a classification accuracy of 99.2% ± 2.6. Furthermore, the obtained accuracy for the other scenarios, such as text-dependent and text-independent audio authentication, is 100% by using the proposed system

Investigation into the impact of governance quality on stock price momentum in international stock markets

Momentum returns are considered an anomaly in the finance literature as their existence cannot be explained by the asset pricing paradigm. This study attempts to shed more light into this anomaly by investigating into the existence and the determinants of momentum returns for a sample of 40 countries worldwide for the period of 1996 to 2018. The key explanation to the momentum returns is about governance quality proxied by World Governance Indicators (WGI) and Corporate Governance Indicators (CGI). Univariate test results reveal a monthly average momentum returns of 0.25 percent with 90 percent of the sample countries exhibit significant momentum effect. Besides, regression analysis shows a negative and significant relationship between WGI and momentum returns. This negative coefficient value supports the prediction of overreaction hypothesis which postulates lower behavioural bias in the market with high governance or institutional quality. Furthermore, the interaction results suggested that the negative impact of governance quality on momentum returns could be altered by the level of information uncertainty faced by individual firms as proxied by trading volume, volatility, size and book-tomarket ratio. There are two distinct contributions to the momentum literature from this study. First, it considers for the first time the impact of country and firm levels governance quality on momentum returns. Second, it is also the first to consider how governance quality can alter the relationship between information uncertainty and behavioural biases with the momentum returns. This study provides two implications; for portfolio managers, as momentum returns are higher in countries with low governance quality, thus portfolios managers should apply momentum strategies in these countries to earn abnormal momentum profits, and; for regulators, governance quality should be strengthened to reduce the abnormal returns that could stabilize the stock market operations

Exact Solutions of Generalized Modified Boussinesq, Kuramoto-Sivashinsky, and Camassa-Holm Equations via Double Reduction Theory

We find exact solutions of the Generalized Modified Boussinesq (GMB) equation, the Kuromoto-Sivashinsky (KS) equation the and, Camassa-Holm (CH) equation by utilizing the double reduction theory related to conserved vectors. The fourth order GMB equation involves the arbitrary function and mixed derivative terms in highest derivative. The partial Noether’s approach yields seven conserved vectors for GMB equation and one conserved for vector KS equation. Due to presence of mixed derivative term the conserved vectors for GMB equation derived by the Noether like theorem do not satisfy the divergence relationship. The extra terms that constitute the trivial part of conserved vectors are adjusted and the resulting conserved vectors satisfy the divergence property. The double reduction theory yields two independent solutions and one reduction for GMB equation and one solution for KS equation. For CH equation two independent solutions are obtained elsewhere by double reduction theory with the help of conserved Vectors

A investigation into share prices’ conditional heteroscedasticity and non-symmetrical model in the context of South Africa, Nigeria, and Egypt

This paper investigates the leverage effect in African countries by applying normal and non-normal distribution densities. Furthermore, we investigate the possible opportunities for portfolio diversification in South Africa, Nigeria, and Egypt. We find that negative stock returns do not generate higher volatility in further returns than past positive returns. All three countries are subject to the ARCH effect, where past stock information (volatility) influence the current stock returns (volatility). We also find that Gaussian distribution produces a better estimate as compared to non-normal distribution. In terms of portfolio diversification, returns are also subject to the ARCH effect, however, the leverage effect does not determine that past negative returns influence the current stock returns asymmetrically

Momentum Effect in Developed and Emerging Stock Markets

The study aims to reaffirm the existence of short-term momentum effect in 13 developed and emerging stock markets where previous literature has a lack of consensus on the issue. Although many studies emphasize on the existence of the momentum effect, still, there is a substantial number of researchers that deny its presence. The contradictory findings of many researchers, over the existence of the momentum effect, raise a serious question as to what extent our stock markets are informationally efficient and whether investors can make abnormal profits by using momentum investment strategies. This study applies the momentum investment strategy, J6K6, to calculate momentum returns. Our study finds a negative significant momentum effect in all 13 stock markets. Although momentum effect is present in 13 countries, yet investors are not able to attain abnormal profit through momentum investing. These findings have utmost importance for practitioners that they should not adopt momentum investment strategies in these countries as these strategies are generating losses. Moreover, stock market regulators should formulate these markets on the notion of an efficient market hypothesis

Automatic Gender Detection Based on Characteristics of Vocal Folds for Mobile Healthcare System

An automatic gender detection may be useful in some cases of a mobile healthcare system. For example, there are some pathologies, such as vocal fold cyst, which mainly occur in female patients. If there is an automatic method for gender detection embedded into the system, it is easy for a healthcare professional to assess and prescribe appropriate medication to the patient. In human voice production system, contribution of the vocal folds is very vital. The length of the vocal folds is gender dependent; a male speaker has longer vocal folds than a female speaker. Due to longer vocal folds, the voice of a male becomes heavy and, therefore, contains more voice intensity. Based on this idea, a new type of time domain acoustic feature for automatic gender detection system is proposed in this paper. The proposed feature measures the voice intensity by calculating the area under the modified voice contour to make the differentiation between males and females. Two different databases are used to show that the proposed feature is independent of text, spoken language, dialect region, recording system, and environment. The obtained results for clean and noisy speech are 98.27% and 96.55%, respectively

Safe Haven or Hedge: Diversification Abilities of Asset Classes in Pakistan

This study compares the safe haven properties of asset classes of real estate (house, plot and residential), gold, dollar, and oil against equity returns in Pakistan for the period January 2011-December 2020. We employ the wavelet coherence to encapsulate the overall dependence and correlation of asset classes. Our results show the dependence is weaker (stronger) in short (long) term investment horizon. We also study the potential of diversification at the tail of returns distribution by applying wavelet value-at-risk (VaR) framework that reveals the degree of co-movement between gold and equity returns greatly affects the portfolio risk followed by residential property and oil. Our findings are beneficial for the individual investor, fund managers and financial advisors looking for the optimal portfolio combination that hedge the excessive negative movements in equity returns subject to the heterogeneity in the investment horizon

Poverty Status and Factors Affecting Household Poverty in Southern Punjab: An Empirical Analysis

The strategies expected to mitigate poverty tend to identify factors that are closely related to poverty and that could have influenced the policy implications. A household level data was collected to examine the poverty status and factors affecting poverty in Southern Punjab. A logistic regression technique was employed for the present analyses. The findings show that age and education of the household head, own house, spouse participation, remittances, number of earners in the household and physical assets reduces the probability of being poor in Southern Punjab. However, large household size, occupation in the primary sector, high dependency ratio and mental disability are associated with an increased probability of being poor in Southern Punjab. Government should adopt effective policy measures to generate employment and encourage the attainment of education for the poor households for the mitigation of poverty in this region

Blind Detection of Copy-Move Forgery in Digital Audio Forensics

Although copy-move forgery is one of the most common fabrication techniques, blind detection of such tampering in digital audio is mostly unexplored. Unlike active techniques, blind forgery detection is challenging, because it does not embed a watermark or signature in an audio that is unknown in most of the real-life scenarios. Therefore, forgery localization becomes more challenging, especially when using blind methods. In this paper, we propose a novel method for blind detection and localization of copy-move forgery. One of the most crucial steps in the proposed method is a voice activity detection (VAD) module for investigating audio recordings to detect and localize the forgery. The VAD module is equally vital for the development of the copy-move forgery database, wherein audio samples are generated by using the recordings of various types of microphones. We employ a chaotic theory to copy and move the text in generated forged recordings to ensure forgery localization at any place in a recording. The VAD module is responsible for the extraction of words in a forged audio, and these words are analyzed by applying a 1-D local binary pattern operator. This operator provides the patterns of extracted words in the form of histograms. The forged parts (copy and move text) have similar histograms. An accuracy of 96.59% is achieved, and the proposed method is deemed robust against noise

An IoT-based smart healthcare system to detect dysphonia

Smart healthcare systems for the internet of things (IoT) platform are cost-efficient and facilitate continuous remote monitoring of patients to avoid unnecessary hospital visits and long waiting times to see practitioners. Presenting a smart healthcare system for the detection of dysphonia can reduce the suffering and pain of patients by providing an initial evaluation of voice. This preliminary feedback of voice could minimize the burden on ENT specialists by referring only genuine cases to them as well as giving an early alarm of potential voice complications to patients. Any possible delay in the treatment and/or inaccurate diagnosis using the subjective nature of tools may lead to severe circumstances for an individual because some types of dysphonia are life-threatening. Therefore, an accurate and reliable smart healthcare system for IoT platform to detect dysphonia is proposed and implemented in this study. Higher-order directional derivatives are used to analyze the time–frequency spectrum of signals in the proposed system. The computed derivatives provide essential and vital information by analyzing the spectrum along different directions to capture the changes that appeared due to malfunctioning the vocal folds. The proposed system provides 99.1% accuracy, while the sensitivity and specificity are 99.4 and 98.1%, respectively. The experimental results showed that the proposed system could provide better classification accuracy than the traditional non-directional first-order derivatives. Hence, the system can be used as a reliable tool for detecting dysphonia and implemented in edge devices to avoid latency issues and protect privacy, unlike cloud processing